A New Method to Model and Predict Progression Free Survival Based on Tumor Growth Dynamics

• Main Authors: Jiajie Yu, Nina Wang, Matts Kågedal
• Format: Article
• Language: English
• Published: Wiley 2020-03-01
• Series: CPT: Pharmacometrics & Systems Pharmacology
• Online Access: https://doi.org/10.1002/psp4.12499

Progression‐free survival (PFS) has been increasingly used as a primary endpoint for early clinical development. The aim of the present work was to develop a model where target lesion dynamics and risk for nontarget progression are jointly modeled for predicting PFS. The model was developed based on a pooled platinum‐resistant ovarian cancer dataset comprising four different treatments and a wide range of dose levels. The target lesion progression was derived from tumor growth dynamics based on the Response Evaluation Criteria in Solid Tumors (RECIST) criteria. The nontarget progression hazard was correlated to the first derivative of target lesion tumor size with respect to time. The PFS time was determined by the first occurring event, target lesion progression, or nontarget progression. The final joint model not only captured target lesion tumor growth dynamics but also predicted PFS well. A similar approach can potentially be used to predict PFS in future oncology studies.